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Dissecting, Imaging, and Modeling
Brain Networks
KOCSEA 2008
October 26, 2008
Yoonsuck ChoeBrain Networks Laboratory
Department of Computer Science
Texas A&M University
Joint work with: Bruce McCormick, Louise Abbott, John Keyser, David Mayerich,
Jaerock Kwon, Donghyeop Han, and Pei-San Huang,
1
Introduction
Main research questions:
1. How does the brain work?
2. How can we use the knowledge to build intelligent artifacts?
Approach:
1. Computational neuroanatomy
Image source: http://www.nervenet.org/papers/Cerebellum2000.html
2
Overview
• Connectomics
• Knife-Edge Scanning Microscope
• Structural reconstruction algorithms
3
Connectomics
4
Connectomics
• Connectome: Complete structural description of the connection
matrix of the brain (see e.g. Sporns et al. 2005).
• Connectomics: Acquisition and mining of the connectome.
• The only available connectome: that of the C. elegans (White
et al. 1986).
Image source: http://www.mouseatlas.org/data/mouse/stages/t47/view
http://www.nervenet.org/papers/Cerebellum2000.html
5
Why Connectomics Research?
• Structure of the nervous system as a foundation of
its function.
– Dynamical properties can be estimated from
static structure.
• Intensive study of single neurons and their molecular
properties must be complemented by a system-level,
architectural perspective.
• Discover modules that make up the brain (motifs,
basic circuits).
• To understand how the brain works!6
Goal of the Project
Obtain and reconstruct the full mouse connectome at
a sub-micrometer resolution.
• 77-78d weight 26–30g
• 13 mm (A-P) × 9.5 mm (M-L) × 6 mm (D-V)
• 75 million neurons (Williams 2000)
7
Knife-Edge Scanning Microscope
8
Knife-Edge Scanning Microscope
• Designed by Bruce H. McCormick.
• Diamond microtome, LM optics, high-speed linescan camera,
precision 3-axis stage [Movie]9
Operational Principles of the KESM
• Aerotech precision stage moves resin-embedded brain tissue across knife
(x/y 20 nm, z 25 nm encoder resolution).
• Back-illumination through diamond knife.
• Nikon CF1 Flour 10X or 40X objectives (NA 0.3/0.8, water imm.).
• Dalsa CT-F3 high-speed line scan camera images the tip of the knife at
44KHz. [Movie]10
KESM Imaging
Line−scan Camera
Microscope objective Diamond knife
Light source
Specimen
Brain specimen is embedded in plastic block.
11
KESM Imaging
Line−scan Camera
Microscope objective Diamond knife
Light source
Specimen
Plastic block is moved toward the knife.
12
KESM Imaging
Line−scan Camera
Microscope objective Diamond knife
Light source
Specimen
Thin tissue slides over knife and gets imaged.
13
KESM Imaging
Line−scan Camera
Microscope objective Diamond knife
Light source
Specimen
Successive line scan constructs a long image.
14
KESM Imaging
Line−scan Camera
Microscope objective Diamond knife
Light source
Specimen
One sweep results in a∼ 4, 000× 20, 000 image (∼ 80MB).
15
KESM Imaging
Line−scan Camera
Microscope objective Diamond knife
Light source
Specimen
One brain results in∼ 25, 000 images.
16
Stair-Step Cutting
Kwon et al. (2008)
• Width of the knife and the field of view of the
objective are not wide enough to cut the entire top
facet of the tissue block.
17
Automated Sectioning/Imaging S/W
• Automated stage controller and image acquisition system
developed in-house.
• Fully automated operation without human intervention: 8 hours a
day, 5 days a week.
18
KESM Data: Golgi Stain
• Mouse cortex (sagittal section).
19
KESM Data: India Ink
• Mouse spinal cord vasculature. [Movie]20
KESM Results: Volume Visualization
Nissl (Cortex) India ink (Spinal cord) Golgi (Pyramidal cell)
Golgi (Cortex) Golgi (Cerebellum) Golgi (Purkinje cell) [Movie]
21
Structural Reconstruction
Algorithms
22
Reconstruction Approaches
Raw data or volume visualization is not enough:
Structural reconstruction is needed.
• Segment-then-connect: the most common approach
• 3D convolutional network: Jain et al. (2007)
• Template-matching-based vector tracing: Al-Kofahi
et al. (2002)
23
Reconstruction: Tracing in 2D
*
inte
nsity
position
ci
ci+1 ci+1
ci
ci+2
step i step i+1
ci+21
2
Choe et al. (2008)
• Moving window with cubic tangential trace spline method.
• Investigates pixels only on the moving window border and on the
interpolated splines for fast processing.
24
Tracing Results
Seed Can et al. (1999)
Haris et al. (1999) Our method25
Robustness Comparison
0
10
20
30
40
50
20 30 40 50
Width
Err
or
0
20
40
60
80
100
120
20 30 40 50
Width
Err
or
Open diamonds: Harris et al.; Closed diamonds: Can et al.; Closed boxes: Our approach.
• Accuracy tested based on synthetic data (by varying
fiber width): Linear (left), curvy (right).
• Much more accurate compared to competing
approaches such as Can et al. (1999); Haris et al.
(1999). 26
Reconstruction: Tracing in 3DMatch!
t = 3
t = 2
t = 1
Template matching Tangential slices Templates
(Mayerich and Keyser 2008; Mayerich et al. 2008)
• Use a moving sphere and trace along points on the
surface of the sphere.
• Use graphics hardware (GPU) for fast matrix
operations during template matching.
27
Tracing Results
Spinal cord vasculature (KESM)
Neuron (Array Tomography, tectum) Vasculature (KESM, cerebellum)28
Speeding Up Tracing Using GPU
0.1
1
10
100
1000
10000
1 10 100 1000 10000
Tim
e (m
s)
Number of Samples
Single Core 2.0GHzQuad Core 2.0GHz
CPU with GPU SamplingFull GPU GeForce 7300
0
5
10
15
20
25
1 10 100 1000 10000
Fact
or
Number of Samples
Single Core 2.0GHzGPU (Sampling Only)
Run time Speedup
• Performance figures demonstrate the speedup
obtained by using GPU computation.
• Speedup achieved by using the full capacity of
GPUs show an almost 20-fold speedup compared to
single-core CPU-based runs.
29
Preliminary Branching Statistics
(vasculature)
Sample Statistics from Reconstructed KESM Brain Vasculature Data (1 mm3 volume)
Region Segments Length Branches Surface Volume Volume
5 5 (mm) (mm2 ) (mm3 ) (% of total)
Neocortex 11459.7 758.5 9100.0 10.40 0.0140 1.4%
Cerebellum 34911.3 1676.4 19034.4 20.0 0.0252 2.5%
Spinal Cord 36791.7 1927.6 26449.1 22.2 0.0236 2.4%
• Geometric structures extracted using the automated
reconstruction algorithms allow us to conduct
quantitative investigation of the structural properties
of brain microstructures.
30
Wrap-Up
31
Discussion and Future Work
• Main contribution: novel imaging method plus
computational algorithms for automated structural
analysis.
• Future work:
– Full-brain reconstruction and validation
– Estimating connectivity from sparsely stained
data (cf. Kalisman et al. 2003)
– Linking structure to function
32
Conclusion
• Understanding brain function requires a system-level
investigation at a microscopic resolution.
• Innovative microscopy technologies are enabling a
data-driven investigation linking the microstructure to
the system.
• The massive data can only be effectively understood
through automated computational algorithms.
33
Acknowledgments
• People:
– Texas A&M: B. McCormick, J. Keyser, L. C. Abbott, D. Mayerich, D.
Han, J. Kwon, Y. H. Bai, D. C.-Y. Eng, H.-F. Yang, G. Kazama, K.
Manavi, W. Koh, Z. Melek, J. S. Guntupalli, P.-S. Huang, A. Aluri, H. S.
Muddana
– Stanford: S. J. Smith, K. Micheva, J. Buchanan, B. Busse
– UCLA: A. Toga
– Others: T. Huffman (Arizona State U), R. Koene (Boston U), Bernard
Mesa (Micro Star Technologies)
• Funded by: NIH/NINDS (#1R01-NS54252); NSF (MRI #0079874 and ITR
#CCR-0220047), Texas Higher Education Coordinating Board (ATP
#000512-0146-2001), and the Department of Computer Science, and the
Office of the Vice President for Research at Texas A&M University.
34
In Memory of Bruce H. McCormick
Bruce H. McCormick (1928–2007)
• Designer of the Knife-Edge Scanning Microscope
35
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